This invention relates to a pattern recognition method.
A technique for detecting and recognizing the position, orientation and shape of a specific object from an image is important for computer vision. Conventionally, various methods for correctly detecting the position and size of an object from an image with complicated background have been considered (see L. G. Brown, “A Survey of Image Registration Techniques,” ACM Computing Surveys, Vol.24, No.4, pp.325–376).
As a feature-based object detection method among those methods, a geometric hashing method is proposed (see Lamdan, Y. and Wolfson, H. J., “Geometric Hashing: a General and Efficient Model-Based Recognition Scheme,” Proceeding of International Conference Computer Vision, pp.238–249, 1988).
In this geometric hashing method, a geometric object is represented by a set of feature points and a model is described using invariant structural representation for translation, scaling and rotation. Then, the object is detected by hypothesis voting and majority rule for the object model, which is similar to Hough transformation (see P. V. C. Hough, “Method and Means for Recognizing Complex Patterns,” U.S. Pat. No. 3,069,654,1962.) In this method, identification and detection of the object can be carried out simultaneously.
Hashing is the name of an algorithm which adds indexing based on a hash function in order to carry out high-speed search for multiple search objects, thereby efficiently dispersing the search objects on a memory and enabling high-speed access to the objects.
On the other hand, several methods based on template matching of intensity change information are proposed as appearance-based approaches (see H. A. Rowley, S. Baluja, and T. Kanade, “Rotational Invariant Neural Network-Based Face Detection,” in Proceedings, IEEE Conference on Computer Vision and Pattern Recognition, pp.38–44, 1998).
Of these methods, matching based on intensity change information uses global information of a whole pattern and therefore is robust against noise.
Various extensions have been made to the geometric hashing method. Although the geometric hashing method is suitable for for detecting geometrically represented objects, it is difficult to detect objects with texture, for example, complicated objects with intensity change information such as eye, nose and mouth parts and shadows on the cheek as in a face shown in FIG. 2-201.
For example, a case will now be considered in which feature point detection as shown in FIG. 2-202 and detection from a set of feature points shown in FIG. 2-203 are carried out. In this case, it is difficult to represent a facial model only by a set of points; and the number of redundant candidate points increases on the complicated background; thus causing errors.
Also the appearance-based approaches, as can be seen from the example of face detection of Rowley et al, requires; multiplexing of model representation such as preparation of multiple templates that have been rotated in advance; and a plurality of time of matching or template matching for several stages of pyramidal images. That is, multiple templates must be provided for a plurality of modifications or transformations. The appearance-based approaches are robust in global evaluation of pattern similarity but are ineffective for discovery of partial distortions and local noise. Meanwhile, there is a method which uses false recognition patterns to improve the detection accuracy, as is carried out in the conventional face region detection methods. This also leaves a problem about how false recognition patterns can be collected.
Requirements for improving these conventional detection methods include the following:
i) registration of partial information and multiple description of models;
ii) a model description method based on invariant in order not to prescribe the orientation; and
iii) a robust detection mechanism against noise.